The use of prescription databases for the study of prescription drug abuse: Prescriptions of Benzodiazepines in Denmark

Transcription

1 The use of prescription databases for the study of prescription drug abuse: Research Unit of General Practice University of Southern Denmark Prescription Drug Abuse Conference, Oslo, November 18-19, 2009

2 Conflicts of interest None to declare

3 Acknowledgment Christiane Gasse Associate Professor National Center for Register-based Research Aarhus University

4 Outline Introduction

5 Regulations Monitoring Relevant epidemiologic measures Danish Guidelines Specific guidance governs prescription of opioids and Benzodiazepines (BZD) Main instructions Use should be limited to 28 days (at most!) Prescriptions only by patients own GP (no doctor shopping) Exception for elderly in long term treatment for sleeping disorders Tightened repeatedly over the last two decades

6 Regulations Monitoring Relevant epidemiologic measures Monitoring by Danish Medicine Agency (DMA) Published report in 2009 on BZD prescriptions (Danish Medicines Agency 2009) Two main measures 1. Number of users during calendar year period prevalence 2. Number of users redeeming more than 28 DDD per year period prevalence of heavy use Adequate for assessment of overall magnitude of problem (?) Inadequate for monitoring effect of interventions

7 Regulations Monitoring Relevant epidemiologic measures (Danish Medicines Agency 2009)

8 Regulations Monitoring Relevant epidemiologic measures (Danish Medicines Agency 2009)

9 Regulations Monitoring Relevant epidemiologic measures

10 Regulations Monitoring Relevant epidemiologic measures Relevant epidemiologic measures Lorenz curves/gini coefficients (Hallas and Stovring 2006) Incidence rates (Støvring et al. 2003) Cessation rates among incident users (Støvring et al. 2007) Types of BZD prescribed for incident users (future work)

11 Danish 25% sample Random 25% sample of all living in Denmark Jan 1, 2005 (n = 1,414,089) Data used here Date of birth Gender Dates of migration (in/out of DK), All BZD prescriptions, (cf. next slide) Future: Linkage with psychiatric diagnoses from Danish hospital discharge data

12 Prescription data ATC codes Anxiolytics: N05BA, N03AE01 Hypnotics and sedatives: N05CD, N05CF Date of redemption Package size and number of tablets Amount of DDDs

13 Lorenz curve Developed to measure economic inequality (income, wealth, etc.) Definition of Lorenz curve Rank subjects in order of income Cumulate income Compute percentiles of rank and cumulated income Yields statements of the type: The top one percent earns 15% of the total income

14 Lorenz curve example Proportion of total income Proportion of individuals

15 Lorenz curve example Proportion of total income Proportion of individuals

16 Gini coefficient Measure of skewness Zero: All have equal income One: One subject only has income Computed from areas of Lorenz curve

17 Computation of Gini coefficient Proportion of total income Gini = A/(A+B) A B Proportion of individuals

18 Lorenz and Gini for BZD Year 2005 Percent cumulative DDD amount Gini coefficient: 64.9% Percent of Benzodiazepine users

19 Lorenz and Gini for BZD Year 2008 Percent cumulative DDD amount Gini coefficient: 68.0% Percent of Benzodiazepine users

20 Percentiles for top users Year 1% top users 10% top users 50% top users

21 Doctor shopping In , the median number of annual prescriptions was 3 In , 10% had 13 or more prescriptions Let us consider subjects with > 12 prescriptions per year (13,587 in 2008) 59% had one GP Id only as prescriber 39% had two to four 2% (293) had five or more

22 Subjects Untreated Redemptions Treated Calendar time

23 Subjects Observed Calendar time

24 Subjects t 0 Calendar time

25 Definitions of treatment status Divide time into quarters of a year Index date is first day of quarter Treatment status on index date determined by prescription(s) in previous date Compute: Prevalence: Proportion of treated on index date Incidence: Proportion of untreated on index date becoming treated Cessation: Proportion of incident in previous quarter who are not treated in next

26 BZD prevalence, quarterly, males Prevalence proportion (/%) Year Trend OR: (0.960; 0.966)

27 BZD prevalence, quarterly, females Prevalence proportion (/%) Year Trend OR: (0.952; 0.956)

28 BZD incidence, quarterly, males Incidence proportion (/%) Year Trend OR: (0.969; 0.978)

29 BZD incidence, quarterly, females Incidence proportion (/%) Year Trend OR: (0.960; 0.966)

30 BZD cessation among new users, quarterly, males Cessation proportion (/%) Year Trend OR: (1.022; 1.047)

31 BZD cessation among new users, quarterly, females Cessation proportion (/%) Year Trend OR: (1.029; 1.047)

32 Preliminary conclusions Clear evidence of potential drug abuse (Lorenz and Gini) Use of BZD declines in Denmark in No clear effect of new regulations in mid-2008 Use should be more closely monitored by central health authorities

33 Future research Stratify on drug class Account for age, diagnosis, etc. Influence of social status Apply more dedicated survival time models

34 Thank you for your attention! Slides prepared with LAT E Xand Beamer

35 Danish Medicines Agency (2009). Significant drop in the consumption of sleeping medicine and anxiolytics in denmark - an analysis of consumption from 2004 to early Technical report, Danish Medicines Agency. Hallas, J. and H. Stovring (2006, Mar). Templates for analysis of individual-level prescription data. Basic Clin Pharmacol Toxicol 98(3), Støvring, H., M. Andersen, H. Beck-Nielsen, A. Green, and W. Vach (2003, Aug). Rising prevalence of diabetes: evidence from a Danish pharmaco-epidemiological database. Lancet 362(9383),

36 Støvring, H., M. Andersen, H. Beck-Nielsen, A. Green, and W. Vach (2007). Counting drugs to understand the disease: The case of measuring the diabetes epidemic. Popul Health Metr 5(1), 2.